Senior AI/ML Engineer for Production ML & RLHF (Remote)

prolificacademicltd; United States; Remote; USD 66,000 - 110,000; Part time

Benefits offered by this job

Competitive pay

Remote-friendly

Flexible schedule

Fast onboarding

Job summary

prolificacademicltd seeks experienced AI/ML Engineers for a remote, asynchronous contributor network. You will tackle paid technical tasks drawing on real-world ML production experience to train and rigorously evaluate large language models, with most sessions fitting within an hour.

Responsibilities include evaluating architecture explanations, inspecting training pipelines, and providing high-quality RLHF feedback to improve model alignment.

Qualifications

• Degree (BS, MS or PhD) in CS, AI, robotics or related quantitative field with ML emphasis.
• Proven experience building, deploying or fine-tuning ML models in production.
• Deep expertise in neural networks (Transformers, CNNs, RNNs) and modern optimization methods.
• Practical experience with prompt engineering, RLHF or retrieval-augmented generation workflows.
• Strong rigor auditing model logic and training data quality; detecting contamination.
• Meticulous attention to detail to identify hallucinations or biased outputs.

Responsibilities

• Evaluate AI-produced explanations of architectures, loss functions and backpropagation for technical soundness.
• Inspect ML code/notebooks, training pipelines and evaluation scripts for efficiency and correctness.
• Provide high-quality human feedback used in RLHF training to improve alignment with intent.
• Trace chain-of-thought reasoning and flag points where model logic collapses.
• Design and execute benchmark tests comparing model outputs against defined metrics.

Skills

ML production

Neural networks

RLHF / prompts

Model evaluation

Code review

Data quality analysis

Education

BS/MS/PhD in CS/AI/Robotics or ML

Tools

PyTorch

TensorFlow/Keras

NumPy/Pandas

Scikit-learn

Hugging Face

Job description

prolificacademicltd seeks experienced AI/ML Engineers for a remote, asynchronous contributor network. You will tackle paid technical tasks drawing on real-world ML production experience to train and rigorously evaluate large language models, with most sessions fitting within an hour.

Responsibilities include evaluating architecture explanations, inspecting training pipelines, and providing high-quality RLHF feedback to improve model alignment.